{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AxYvBv9PqrSH"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "import yfinance as yf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92d4SoLUrDfm"
      },
      "outputs": [],
      "source": [
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VdFKD8avs5AB"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pickle\n",
        "from tqdm.notebook import tnrange"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MpbJko9s6uq",
        "outputId": "cbf99518-3f99-4c2b-eb25-493218c3a857"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "data = yf.download(\"AAPL\" , start = \"2019-01-01\" , interval = '1d')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDIT32ges8m0",
        "outputId": "de74fbe5-7e70-4f43-90be-9ea4f7cd8ae5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1126, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "17LL6zne-3zK",
        "outputId": "8c76bd1c-1957-40f4-f308-58456b48eaf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2023-06-15  183.960007  186.520004  183.779999  186.009995  186.009995   \n",
              "2023-06-16  186.729996  186.990005  184.270004  184.919998  184.919998   \n",
              "2023-06-20  184.410004  186.100006  184.410004  185.009995  185.009995   \n",
              "2023-06-21  184.899994  185.410004  182.589996  183.960007  183.960007   \n",
              "2023-06-22  183.740005  187.050003  183.669998  187.000000  187.000000   \n",
              "\n",
              "               Volume  \n",
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              "2023-06-15   65433200  \n",
              "2023-06-16  101235600  \n",
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              "      <th>2023-06-22</th>\n",
              "      <td>183.740005</td>\n",
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              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2019-01-02  38.722500  39.712502  38.557499  39.480000  37.994492  148158800\n",
              "2019-01-03  35.994999  36.430000  35.500000  35.547501  34.209961  365248800\n",
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              "          const element = document.querySelector('#df-2295fee6-fd80-4fa2-b426-7a50fff37edc');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      "source": [
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              "              Open         High          Low        Close    Adj Close  \\\n",
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              "\n",
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              "std    5.275813e+07  \n",
              "min    3.519590e+07  \n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1126.000000</td>\n",
              "      <td>1.126000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>115.728528</td>\n",
              "      <td>117.137349</td>\n",
              "      <td>114.430773</td>\n",
              "      <td>115.856772</td>\n",
              "      <td>114.556519</td>\n",
              "      <td>1.069938e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.788278</td>\n",
              "      <td>44.287583</td>\n",
              "      <td>43.297391</td>\n",
              "      <td>43.813022</td>\n",
              "      <td>44.050809</td>\n",
              "      <td>5.275813e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>35.994999</td>\n",
              "      <td>36.430000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>35.547501</td>\n",
              "      <td>34.209961</td>\n",
              "      <td>3.519590e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>70.560627</td>\n",
              "      <td>71.589373</td>\n",
              "      <td>69.831875</td>\n",
              "      <td>71.077497</td>\n",
              "      <td>69.472042</td>\n",
              "      <td>7.396980e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>128.834999</td>\n",
              "      <td>130.464996</td>\n",
              "      <td>127.035000</td>\n",
              "      <td>129.009995</td>\n",
              "      <td>127.045498</td>\n",
              "      <td>9.250255e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>149.879997</td>\n",
              "      <td>151.487503</td>\n",
              "      <td>148.470005</td>\n",
              "      <td>150.369995</td>\n",
              "      <td>149.487064</td>\n",
              "      <td>1.241381e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>186.729996</td>\n",
              "      <td>187.050003</td>\n",
              "      <td>184.410004</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>4.265100e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c991f808-0884-49d7-bd8d-737cf36d55ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c991f808-0884-49d7-bd8d-737cf36d55ad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c991f808-0884-49d7-bd8d-737cf36d55ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Get the statistics of the data\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "L3EP50d_tBer",
        "outputId": "5137162b-a63a-4ccc-9fc4-f179c15be285"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('66fef9be-fc37-4a11-bccc-ef160a7baa08');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "# Check the trend in Closing Values\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = data['Close'] , mode = 'lines'))\n",
        "fig.update_layout(height = 500 , width = 900,\n",
        "                  xaxis_title='Date' , yaxis_title='Close')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "qZ__r7jPtDTD",
        "outputId": "f409bea4-ae36-4a74-c70b-f77f43f09396"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check the trend in Volume Traded\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = data['Volume'] , mode = 'lines'))\n",
        "fig.update_layout(height = 500 , width = 900,\n",
        "                  xaxis_title='Date' , yaxis_title='Volume')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dFIOcWtstFZq"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Normalize the data\n",
        "data_close = data[\"Close\"].values\n",
        "data_mean = np.mean(data_close)\n",
        "data_std = np.std(data_close)\n",
        "data_close = (data_close - data_mean) / data_std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fij-fA-_tHdn"
      },
      "outputs": [],
      "source": [
        "# Confirm the Testing Set length\n",
        "test_length = data[(data.index >= '2022-09-01')].shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DM1bNqiotJTZ"
      },
      "outputs": [],
      "source": [
        "def CreateFeatures_and_Targets(data, feature_length):\n",
        "    X = []\n",
        "    Y = []\n",
        "\n",
        "    for i in tnrange(len(data) - feature_length):\n",
        "        X.append(data.iloc[i : i + feature_length,:].values)\n",
        "        Y.append(data[\"Close\"].values[i+feature_length])\n",
        "\n",
        "    X = np.array(X)\n",
        "    Y = np.array(Y)\n",
        "\n",
        "    return X , Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bVY4k2wMtnu5"
      },
      "outputs": [],
      "source": [
        "# Prepare the data\n",
        "lookback = 50\n",
        "X = []\n",
        "Y = []\n",
        "for i in range(len(data_close) - lookback - 1):\n",
        "    X.append(data_close[i:(i+lookback)])\n",
        "    Y.append(data_close[i+lookback])\n",
        "X = np.array(X)\n",
        "Y = np.array(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jziW_s3quvi_"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Split the data into training and testing sets\n",
        "train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lf64cDGeu1cQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "class MultiDimensionScaler():\n",
        "    def __init__(self):\n",
        "        self.scalers = []\n",
        "\n",
        "    def fit_transform(self, X):\n",
        "        total_dims = X.shape[1]  # Get the second dimension of X\n",
        "        for i in range(total_dims):\n",
        "            Scaler = MinMaxScaler()\n",
        "            X[:, i] = Scaler.fit_transform(X[:, i].reshape(-1, 1)).flatten()\n",
        "            self.scalers.append(Scaler)\n",
        "        return X\n",
        "\n",
        "    def transform(self, X):\n",
        "        for i in range(X.shape[1]):\n",
        "            X[:, i] = self.scalers[i].transform(X[:, i].reshape(-1, 1)).flatten()\n",
        "        return X\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TvkQhe1Bwe4_"
      },
      "outputs": [],
      "source": [
        "Feature_Scaler = MultiDimensionScaler()\n",
        "train_X = Feature_Scaler.fit_transform(train_X)\n",
        "test_X = Feature_Scaler.transform(test_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0xonMigQwhMx"
      },
      "outputs": [],
      "source": [
        "Target_Scaler = MinMaxScaler()\n",
        "train_Y = Target_Scaler.fit_transform(train_Y.reshape(-1,1))\n",
        "test_Y = Target_Scaler.transform(test_Y.reshape(-1,1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mj3Xg-mrwkEK"
      },
      "outputs": [],
      "source": [
        "def save_object(obj , name : str):\n",
        "    pickle_out = open(f\"{name}.pck\",\"wb\")\n",
        "    pickle.dump(obj, pickle_out)\n",
        "    pickle_out.close()\n",
        "\n",
        "def load_object(name : str):\n",
        "    pickle_in = open(f\"{name}.pck\",\"rb\")\n",
        "    data = pickle.load(pickle_in)\n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WGTIi7CVwmG0"
      },
      "outputs": [],
      "source": [
        "# Save your objects for future purposes\n",
        "save_object(Feature_Scaler , \"Feature_Scaler\")\n",
        "save_object(Target_Scaler , \"Target_Scaler\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Mwo5yofwoFC",
        "outputId": "91feff1c-5f28-4e0e-e409-145fc9aaa36f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train X shape: (860, 50)\n",
            "Train Y shape: (860, 1)\n",
            "Test X shape: (215, 50)\n",
            "Test Y shape: (215, 1)\n"
          ]
        }
      ],
      "source": [
        "# Ensure the sizes of training and testing sets\n",
        "print(\"Train X shape:\", train_X.shape)\n",
        "print(\"Train Y shape:\", train_Y.shape)\n",
        "print(\"Test X shape:\", test_X.shape)\n",
        "print(\"Test Y shape:\", test_Y.shape)\n",
        "\n",
        "train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))\n",
        "test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dM9OkcskwqPY"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
        "\n",
        "save_best = ModelCheckpoint(\"new_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Concatenate, Input\n",
        "from keras.models import Model\n",
        "\n",
        "# Define the input layer shape for both LSTM and CNN\n",
        "input_layer = Input(shape=(train_X.shape[1], 1))\n",
        "\n",
        "# Build the LSTM model\n",
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(train_X.shape[1], 1)))\n",
        "lstm_model.add(Dropout(0.2))\n",
        "lstm_model.add(LSTM(units=50))\n",
        "lstm_model.add(Dropout(0.2))\n",
        "lstm_model.add(Dense(units=1))\n",
        "\n",
        "# Build the CNN model\n",
        "cnn_model = Sequential()\n",
        "cnn_model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(train_X.shape[1], 1)))\n",
        "cnn_model.add(MaxPooling1D(pool_size=2))\n",
        "cnn_model.add(Flatten())\n",
        "cnn_model.add(Dense(units=50, activation='relu'))\n",
        "\n",
        "# Concatenate the output from LSTM and CNN\n",
        "concat_layer = Concatenate()([cnn_model(input_layer) ,lstm_model(input_layer)])\n",
        "\n",
        "# Build the final model\n",
        "output_layer = Dense(units=1, activation='linear')(concat_layer)\n",
        "model = Model(inputs=input_layer, outputs=output_layer)\n",
        "\n",
        "# Compile and train the CNN model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(train_X, train_Y, epochs=10, batch_size=32,validation_data=(test_X , test_Y),\n",
        "            callbacks=[reduce_lr , save_best])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBbGGOFgtDAI",
        "outputId": "22fb3c15-fb06-4984-aafa-77218bc74e7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "27/27 [==============================] - 9s 134ms/step - loss: 0.0298 - val_loss: 0.0090 - lr: 0.0010\n",
            "Epoch 2/10\n",
            "27/27 [==============================] - 1s 56ms/step - loss: 0.0043 - val_loss: 0.0031 - lr: 0.0010\n",
            "Epoch 3/10\n",
            "27/27 [==============================] - 2s 57ms/step - loss: 0.0031 - val_loss: 0.0028 - lr: 0.0010\n",
            "Epoch 4/10\n",
            "27/27 [==============================] - 1s 56ms/step - loss: 0.0028 - val_loss: 0.0027 - lr: 0.0010\n",
            "Epoch 5/10\n",
            "27/27 [==============================] - 2s 57ms/step - loss: 0.0025 - val_loss: 0.0018 - lr: 0.0010\n",
            "Epoch 6/10\n",
            "27/27 [==============================] - 1s 55ms/step - loss: 0.0019 - val_loss: 0.0016 - lr: 0.0010\n",
            "Epoch 7/10\n",
            "27/27 [==============================] - 2s 57ms/step - loss: 0.0017 - val_loss: 0.0012 - lr: 0.0010\n",
            "Epoch 8/10\n",
            "27/27 [==============================] - 2s 87ms/step - loss: 0.0015 - val_loss: 9.9446e-04 - lr: 0.0010\n",
            "Epoch 9/10\n",
            "27/27 [==============================] - 2s 65ms/step - loss: 0.0011 - val_loss: 0.0010 - lr: 0.0010\n",
            "Epoch 10/10\n",
            "27/27 [==============================] - 1s 55ms/step - loss: 0.0011 - val_loss: 8.6258e-04 - lr: 0.0010\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f2b5826a5c0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights(\"new_weights.h5\")\n",
        "model.load_weights(\"new_weights.h5\")"
      ],
      "metadata": {
        "id": "9GM45WZdqm88"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the model Structure\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "n8LyYfxV9I2u",
        "outputId": "3d76c8f2-7cc6-4edb-a992-fb9ca4e388bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 50, 1)]      0           []                               \n",
            "                                                                                                  \n",
            " sequential_1 (Sequential)      (None, 50)           77106       ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " sequential (Sequential)        (None, 1)            30651       ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 51)           0           ['sequential_1[0][0]',           \n",
            "                                                                  'sequential[0][0]']             \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 1)            52          ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 107,809\n",
            "Trainable params: 107,809\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egizzcpA2CV-",
        "outputId": "deef896a-8566-43e3-a7d4-2da82dd9c5ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 1s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "Predictions = model.predict(test_X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wHxKo4_i2YO7"
      },
      "outputs": [],
      "source": [
        "Predictions = Target_Scaler.inverse_transform(Predictions)\n",
        "Actual = Target_Scaler.inverse_transform(test_Y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of the arrays before squeezing\n",
        "print(Predictions.shape)\n",
        "print(Actual.shape)\n",
        "\n",
        "# Reshape the arrays to remove the axis you don't need\n",
        "Predictions = Predictions.reshape(-1)\n",
        "Actual = Actual.reshape(-1)\n",
        "\n",
        "# Alternatively, you can squeeze the axis with the smallest size\n",
        "if Predictions.shape[0] == 1:\n",
        "    Predictions = np.squeeze(Predictions, axis=0)\n",
        "if Actual.shape[0] == 1:\n",
        "    Actual = np.squeeze(Actual, axis=0)\n"
      ],
      "metadata": {
        "id": "lEef_G3hys4w",
        "outputId": "5cdd1e8a-bb80-4ef2-faf7-c5e2550250ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(215, 1)\n",
            "(215, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dPrnLyUW2cQQ"
      },
      "outputs": [],
      "source": [
        "# Confirm the Testing Set length\n",
        "test_length = data[(data.index >= '2022-12-10')].shape[0]\n",
        "\n",
        "# Creating Sample Test Dataframe\n",
        "test_dataframe_dict = {'Actual': Actual[-test_length:], 'Predicted': Predictions[-test_length:]}\n",
        "test_df = pd.DataFrame(test_dataframe_dict)\n",
        "\n",
        "test_df.index = data.index[-test_length:]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "RdOnXLKA2d5H",
        "outputId": "9fabf41b-cce9-42b8-d055-ddb6f94cb202"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "              Actual  Predicted\n",
              "Date                           \n",
              "2022-12-12 -0.881848  -0.790369\n",
              "2022-12-13 -1.030158  -1.043891\n",
              "2022-12-14  1.286336   1.369719\n",
              "2022-12-15  0.418400   0.181145\n",
              "2022-12-16  1.367398   1.182939"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Check the trend in Volume Traded\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(x = test_df.index , y = Actual , mode = 'lines' , name='Actual'))\n",
        "fig.add_trace(go.Scatter(x = test_df.index , y = Predictions , mode = 'lines' , name='Predicted'))\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Total_features = np.concatenate((train_X , test_X) , axis = 0)"
      ],
      "metadata": {
        "id": "P7NH3fhg2KiL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Total_Targets = np.concatenate((train_Y , test_Y) , axis = 0)"
      ],
      "metadata": {
        "id": "99RLUYIp2MWr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Predictions = model.predict(Total_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhNyyXt9ZKE1",
        "outputId": "b5b20b17-3660-4e89-d8de-ff57e7229379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34/34 [==============================] - 1s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Predictions = Target_Scaler.inverse_transform(Predictions)\n",
        "Actual = Target_Scaler.inverse_transform(Total_Targets)"
      ],
      "metadata": {
        "id": "3lBzzxWXYKGg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of the arrays before squeezing\n",
        "print(Predictions.shape)\n",
        "print(Actual.shape)\n",
        "\n",
        "# Reshape the arrays to remove the axis you don't need\n",
        "Predictions = Predictions.reshape(-1)\n",
        "Actual = Actual.reshape(-1)\n",
        "\n",
        "# Alternatively, you can squeeze the axis with the smallest size\n",
        "if Predictions.shape[0] == 1:\n",
        "    Predictions = np.squeeze(Predictions, axis=0)\n",
        "if Actual.shape[0] == 1:\n",
        "    Actual = np.squeeze(Actual, axis=0)\n"
      ],
      "metadata": {
        "id": "BKBGhLtfY2su",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8ef16f2-4a66-4004-fc2b-9d0397c8e5b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1075, 1)\n",
            "(1075, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the trend in Volume Traded\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x = data.index , y = Actual , mode = 'lines' , name='Actual'))\n",
        "fig.add_trace(go.Scatter(x = data.index , y = Predictions , mode = 'lines' , name='Predicted'))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Zl4eTfQHZWNR",
        "outputId": "5cc8f714-37bd-41ba-c628-9fe77f736387"
      },
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn_preds = model.predict(test_X)"
      ],
      "metadata": {
        "id": "ATiMsQX5ZZqk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29f2e118-8282-40bf-fd5c-109e1a299f53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 15ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = np.mean((test_Y - cnn_preds) ** 2)"
      ],
      "metadata": {
        "id": "1WMK7jT1JMnD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "CTqIiHReJlXj",
        "outputId": "df508b17-075a-481c-82cb-3854ad15ebdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0008625825064076412"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
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      "source": [
        "rmse = np.sqrt(mse)"
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      "metadata": {
        "id": "HHp5nV_0JglR"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "tyrSnSTrJk3_",
        "outputId": "da094622-5514-4da7-9936-90ccaacf43c9"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "0.029369754959952272"
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          "metadata": {},
          "execution_count": 42
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      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data\n",
        "test_loss = model.evaluate(test_X, test_Y)\n",
        "\n",
        "# Print the test loss\n",
        "print('Test loss:', test_loss)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9NFLlyCJ8Z1",
        "outputId": "2aa7fae7-46c5-47db-c6b8-2bd897eeddb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 18ms/step - loss: 8.6258e-04\n",
            "Test loss: 0.0008625824120827019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VydUooy_Lxh4"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
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    "kernelspec": {
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      "name": "python3"
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